Meta Interview Questions — Page 53

Meta·Data Scientist·Onsite - Product Sense / Strategy

Apr 2026

Brutal product analytics case for a Meta DS role, all five parts in one session. The questions were deeply technical and interconnected, which I did not expect. Walked out unsure if I'd nailed any of it.

Using only product analytics data (no surveys), how would you quantify tie strength and group affinity from historical messaging and call data to identify which users actually need a group video call feature? Walk through specific metrics and how you'd segment users to produce a ranked candidate list for launch targeting.
You can't run a user survey. Design the exact event logging schema you'd ask engineering to instrument in order to infer latent demand for group calls from recipient behavior. For each event, specify the event name, triggering action, required fields, sampling constraints, and how you'd convert the logs into leading indicators.
Pick one existing Meta product to use as a proxy for forecasting group video call adoption and operational risks. Justify the choice, define the exact proxy metrics you'd read, and explain how you'd translate those numbers into targets while accounting for platform differences.
Should the group video call feature enforce a maximum participant cap at launch? Build a data-backed decision framework using historical multi-party interaction distributions, infrastructure capacity, call quality trade-offs, and safety considerations. Propose an initial cap and a ramp playbook with guardrail metrics and stop conditions.
Design an experiment to measure the incremental value of the group video call feature, accounting for network effects and contamination. Cover randomization unit, interference mitigation, exposure policy for treated-to-control calls, expected contamination math, primary KPIs, guardrails, power analysis inputs, a staged ramp plan, and how you'd measure direct versus indirect network effects.

“This one tripped me up because I kept reaching for survey-based proxies before catching myself.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta SWE coding round, one question on string manipulation with parentheses. Pretty standard stack problem but the output requirements tripped me up a bit.

Given a string containing lowercase letters and parentheses, remove the fewest parentheses possible to make the string valid. Return both the count of removals and the resulting string.

“My first instinct was to just scan left to right and track unmatched parens with a counter, but then I realized I also needed to reconstruct the actual string, not just count removals.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026

Stats-heavy technical screen for a Data Scientist role at Meta. The whole session basically revolved around one multi-part question on sampling theory and confidence intervals, which sounds manageable until you're actually on the spot trying to keep CLT, expected value, and two different standard deviation definitions straight at the same time.

Walk through how the Central Limit Theorem applies when computing average comments per user from a sample. Then define and calculate the expected value of comments across all users, explain the difference between population and sample standard deviation in this context, and build a 95% confidence interval for the mean.

“A lot packed into one question.”

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Meta·Software Engineer·Onsite - Behavioral / Leadership

Apr 2026

Behavioral round at Meta for a software engineer role, focused on conflict and disagreement with teammates or stakeholders. Just the one question but they really wanted the full story, not a surface-level answer.

Tell me about a significant conflict or disagreement you had with a teammate, stakeholder, or manager where you thought your approach was the right one. Walk through the situation, what you were trying to accomplish, how you made your case, and what ended up happening.

“The part I fumbled was the 'how you persuaded them' section.”

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Meta·Data Scientist·Technical Phone Screen

Apr 2026

A deep product experimentation interview for a data scientist role at Meta. The whole thing was structured around a single feature design prompt, but it went way deeper than I expected, covering stats, logging, revenue modeling, and even launch decision-making.

You're proposing a new Instagram feature called shareable Collections 2.0. How would you define success for it, including a North Star metric for engagement, one for revenue, and at least three counter metrics?
Would you run a user-level or geo-clustered A/B test for this feature, and why? How would you handle network effects and contamination? Describe your stratification and randomization approach.
With a baseline D30 retention of 30% and a target of 1% relative lift, calculate the required sample size per arm at 90% power and alpha 0.05. Walk through your assumptions and the formula you'd use.
What stopping rules, pre-registration requirements, and guardrail thresholds would you define before launching the experiment?
Describe the logging spec you'd write for this feature and how you'd attribute revenue. Then estimate the weekly incremental revenue if the feature increases ad clicks by 2% across 100 million DAUs, given 4 ad impressions per DAU per day, 1% CTR, and $0.50 revenue per click.
Even if the experiment shows statistically significant positive results, what reasons might you have to not launch the feature?

“I went with creator post frequency as the engagement north star and incremental ad revenue per DAU for monetization.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Meta PM interview, one question about the future direction of iOS. Pretty open-ended, which sounds freeing until you're actually in the hot seat trying to say something that isn't just 'better battery life'.

How would you improve iOS over the next 3 to 5 years?

“I went straight to features and kind of rambled through a list before realizing I hadn't set up any framework for why those things mattered.”

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Meta·Product Manager·Onsite - Product Sense / Strategy

Apr 2026

Meta PM interview with a product design question about building a skill-matching platform between learners and mentors. Pretty open-ended, which I wasn't fully prepared for.

Design a platform that connects people who want to learn new skills with mentors who can teach them.

“I jumped straight into features and had to backtrack to define the user segments, which probably cost me a minute or two of credibility.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta interview for a Supply Chain Capacity Engineer role, focused on a string algorithm problem with a side conversation about complexity tradeoffs. Pretty straightforward coding screen but the follow-up discussion on approaches caught me a bit flat-footed.

Given a string, find the shortest substring that appears exactly once in it. If there are ties in length, return any one (or the lexicographically smallest, depending on what the interviewer wants).

“I started with the brute force: enumerate all substrings and count occurrences, which is roughly cubic time.”

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Meta·Software Engineer·Onsite - System Design / Architecture

Apr 2026

System design round at Meta for a software engineering role. The question was a full banking system design, which sounds scoped but really isn't once they start pulling on every thread.

Design a banking system that supports account creation, balance inquiry, deposit, withdrawal, and atomic transfers between accounts including cross-currency. Cover the API design, data model, and transaction flow.
How do you guarantee ACID properties for transfers, handle idempotent retries, manage concurrency, and prevent overdrafts?
How would you ensure durability, auditability, and consistency across services, and how do you approach reconciliation?
How would you scale this system using sharding or partitioning, and how do you handle failures using sagas versus distributed transactions?
What security considerations apply here, including authentication, authorization, encryption, rate limiting, and PCI compliance?
How would you monitor this system and what signals would you track to detect problems early?

“I started with the happy path and the interviewer immediately asked about cross-currency transfers, which I hadn't fully thought through.”

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Meta·Software Engineer·Technical Phone Screen

Apr 2026

Meta QA engineer interview, one question about debugging a gnarly technical bug. Pretty straightforward as far as these go, though it's the kind of question that sounds easy until you're actually sitting there trying to pick the right story.

Walk me through the most technically complex bug or issue you've encountered and how you went about debugging it.

“I spent too long picking which story to tell and ended up rushing the actual explanation.”

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